methodatlas
Method guideAgile

NoEstimates

A practical method guide: clarify prerequisites and materials, understand the complete process, and look up outputs, pitfalls, and stop criteria.

ComplexityMedium
Timelaufend
Participants2-12
FormatWorkshop + async
MaturityEstablished
Plan NoEstimates
01

Prerequisite

What needs to be finished first

Complete firstComparable subject

Close decision need, service boundary, and Done; slice work so items flow comparably; check historical data quality.

Without a common frame, values are not comparable.
Complete firstEvidence access

Small value-oriented items, explicit Ready/Done policies, WIP limits, throughput and cycle-time history, probabilistic forecast

Missing data remains visible as uncertainty.
02

Preparation

What needs to be ready before start

Materials

Small value-oriented items, explicit Ready/Done policies, WIP limits, throughput and cycle-time history, probabilistic forecast

People / roles

Product/service owner · delivery team · flow manager · data owner · stakeholders

Pre-read

Close decision need, service boundary, and Done; slice work so items flow comparably; check historical data quality.

Time needed

Half-day setup plus weekly replenishment, flow, and forecast reviews.

Setup

Prepare a workspace with Purpose · Slice · Control · Measure · Forecast · Learn; separate example from live data.

03

Core question

The one question this method answers

How can the team make planning and delivery decisions with small work items, flow data, and frequent reforecasting without estimating every item upfront?

04

Flow

Phases with goal, actions, output and pitfall

Sie reduziert den Bedarf an Vorabschätzung und steuert Arbeit über kleine, gleichmäßige Einheiten und sichtbaren Fluss. So sinkt Aufwand im Planning, während Lieferfähigkeit und Transparenz erhalten bleiben und Engpässe schneller auffallen.

NoEstimates · working structure

Fictional example

Fictional values show the actual method fields.

NoEstimates · working structure
System elementPolicy/dataFictional stateDecision signalActionOwnerReview
Item slicingAdd evidence and rationale.Split policyValuable; one Done; provider migration in 4 itemsItem age >8 daysSplit before pullProduct + teamReplenishment
WIP/pullAdd evidence and rationale.Pull policyLimit 4; pull only with free slotBlocked >2 daysAssign swarm ownerFlow managerDaily
Flow dataAdd evidence and rationale.12-week windowThroughput 5,7,4,6,8,5,0,7,6,4,9,5Zero week remains a sampleRecord cause; retain valueData ownerWeekly
ForecastAdd evidence and rationale.Date for quantity30 items: P50 6, P85 7, P95 8 weeksP85 drifts beyond 8 weeksDecide scope/WIPService ownerWeekly
  1. 1. Policy/data

    Item slicing
    Split policy
    WIP/pull
    Pull policy
    Flow data
    12-week window
    Forecast
    Date for quantity
  2. 2. Fictional state

    Item slicing
    Valuable; one Done; provider migration in 4 items
    WIP/pull
    Limit 4; pull only with free slot
    Flow data
    Throughput 5,7,4,6,8,5,0,7,6,4,9,5
    Forecast
    30 items: P50 6, P85 7, P95 8 weeks
  3. 3. Decision signal

    Item slicing
    Item age >8 days
    WIP/pull
    Blocked >2 days
    Flow data
    Zero week remains a sample
    Forecast
    P85 drifts beyond 8 weeks
  4. 4. Action

    Item slicing
    Split before pull
    WIP/pull
    Assign swarm owner
    Flow data
    Record cause; retain value
    Forecast
    Decide scope/WIP
  5. 5. Owner

    Item slicing
    Product + team
    WIP/pull
    Flow manager
    Flow data
    Data owner
    Forecast
    Service owner
  6. 6. Review

    Item slicing
    Replenishment
    WIP/pull
    Daily
    Flow data
    Weekly
    Forecast
    Weekly

All values are fictional.

Reference · original teaching example

  1. 1

    Purpose

    Planning question

    Goal

    Planning question: Set which decisions will be supported without item estimates and define service boundary.

    Inputs for this step

    • Close decision need, service boundary, and Done; slice work so items flow comparably; check historical data quality.
    • Small value-oriented items, explicit Ready/Done policies, WIP limits, throughput and cycle-time history, probabilistic forecast

    Actions

    • Set which decisions will be supported without item estimates and define service boundary.

    Output

    Planning question

    Set which decisions will be supported without item estimates and define service boundary.

    Quality check

    Is it clear whether answering quantity-by-date or date-for-quantity?

    Pitfall

    NoEstimates is declared an end to planning. Name concrete decision question, risk level, and review cadence.

    Timing: 30–45 minutes

  2. 2

    Slice

    Flowable items

    Goal

    Flowable items: Shape work into small valuable items with one Done and explicit split policy.

    Inputs for this step

    • Planning question

    Actions

    • Shape work into small valuable items with one Done and explicit split policy.

    Output

    Flowable items

    Shape work into small valuable items with one Done and explicit split policy.

    Quality check

    Are large or unlike items detectable and splittable before pull?

    Pitfall

    Large epics flow as one counted item and distort data. Limit item age/size and split oversized work before start.

    Timing: 45–60 minutes

  3. 3

    Control

    Flow system

    Goal

    Flow system: Operate pull, WIP limits, replenishment, and blocker policy; reprioritise scope continuously.

    Inputs for this step

    • Flowable items

    Actions

    • Operate pull, WIP limits, replenishment, and blocker policy; reprioritise scope continuously.

    Output

    Flow system

    Operate pull, WIP limits, replenishment, and blocker policy; reprioritise scope continuously.

    Quality check

    Are new items pulled only with capacity and blockers actively ended?

    Pitfall

    Without estimation meetings, work starts without planning. Make selection, pull, and blocker rules explicit.

    Timing: Ongoing

  4. 4

    Measure

    Flow data

    Goal

    Flow data: Capture throughput, cycle time, WIP, and item age from consistent start/Done events.

    Inputs for this step

    • Flow system

    Actions

    • Capture throughput, cycle time, WIP, and item age from consistent start/Done events.

    Output

    Flow data

    Capture throughput, cycle time, WIP, and item age from consistent start/Done events.

    Quality check

    Is data complete, service-consistent, and free from retrospective redefinition?

    Pitfall

    Average velocity simply replaces item estimates. Preserve empirical distribution and causes of outliers.

    Timing: Weekly

  5. 5

    Forecast

    Delivery forecast

    Goal

    Delivery forecast: Forecast remaining count probabilistically from historical flow and test scope/WIP scenarios.

    Inputs for this step

    • Flow data

    Actions

    • Forecast remaining count probabilistically from historical flow and test scope/WIP scenarios.

    Output

    Delivery forecast

    Forecast remaining count probabilistically from historical flow and test scope/WIP scenarios.

    Quality check

    Does forecast state quantile, data window, and assumptions?

    Pitfall

    One date is presented as a commitment. Show multiple quantiles and tie decision to accepted risk.

    Timing: Weekly

  6. 6

    Learn

    Flow decision

    Goal

    Flow decision: Compare forecast to actual, decide scope/policies, and justify new data window after system change.

    Inputs for this step

    • Delivery forecast

    Actions

    • Compare forecast to actual, decide scope/policies, and justify new data window after system change.

    Output

    Flow decision

    Compare forecast to actual, decide scope/policies, and justify new data window after system change.

    Quality check

    Does variance lead to a scope, WIP, or capability decision?

    Pitfall

    The team improves estimate values rather than flow. Improve blockers, ageing, and slicing; then update forecast.

    Timing: Weekly

05

Artifact

What comes out at the end

Sketch: what the artifact looks like

NoEstimates · Flow statt AufwandsschätzungPlanungsfrage klären, Arbeit klein schneiden und Forecasts aus Durchsatz, Cycle Time sowie historischen Flow-Daten ableiten
NoEstimatesDas Visual zeigt NoEstimates als Flow-Ansatz mit Planungsfrage, kleinen Items, Durchsatzdaten und Forecast statt Aufwandspunkten.Planung über Flow-Daten statt über Aufwandspunkte steuernNoEstimates ersetzt unnötige Schätzrituale durch kleine Arbeitseinheiten, gemessenen Durchsatz und regelmäßige Kalibrierung.PlanungsfrageWelche Entscheidung braucht wirklicheine Schätzung?Kleine Itemsähnlich schneiden, zählen stattschätzenFlow-Daten statt Aufwandspunkte123456Throughput & Cycle Timehistorische Lieferung beobachtenFlow ForecastWahrscheinlichkeit statt EinzelzusageSchätzritual nur nutzen, wenn es Entscheidungswert hatArbeit klein haltenForecast regelmäßig kalibrieren
Form

Operable NoEstimates system with small items, pull/WIP rules, replenishment, flow metrics, probabilistic forecasts, and scope decisions.

Versioning / ownership

Record date, team, data state, and review date; preserve prior versions.

Tool alternatives
  • Item-slicing rules
  • WIP/pull board
  • Throughput and cycle-time data
  • Forecast and decision log

no-estimates-working-template.md

Set subject, data, roles, flow, and review.

NoEstimates: Worksheet

Question

To be confirmed

Desired outcome

To be confirmed

Scope

Whole initiative Time: Half-day setup plus weekly replenishment, flow, and forecast reviews.

Complete this worksheet on paper or in your own document during the work.

Preparation for this scope

Method setup

  • Materials: Small value-oriented items, explicit Ready/Done policies, WIP limits, throughput and cycle-time history, probabilistic forecast
  • Roles: Product/service owner · delivery team · flow manager · data owner · stakeholders
  • Advance information: Close decision need, service boundary, and Done; slice work so items flow comparably; check historical data quality.
  • Overall time needed: Half-day setup plus weekly replenishment, flow, and forecast reviews.
  • Setup: Prepare a workspace with Purpose · Slice · Control · Measure · Forecast · Learn; separate example from live data.

Prepare for the work steps

Purpose: Planning question

  • Close decision need, service boundary, and Done; slice work so items flow comparably; check historical data quality.
  • Small value-oriented items, explicit Ready/Done policies, WIP limits, throughput and cycle-time history, probabilistic forecast

Slice: Flowable items

  • Planning question

Control: Flow system

  • Flowable items

Measure: Flow data

  • Flow system

Forecast: Delivery forecast

  • Flow data

Learn: Flow decision

  • Delivery forecast

NoEstimates · working structure

System elementPolicy/dataFictional stateDecision signalActionOwnerReview
Item slicing
WIP/pull
Flow data
Forecast
  • Item slicing: Add evidence and rationale.
  • WIP/pull: Add evidence and rationale.
  • Flow data: Add evidence and rationale.
  • Forecast: Add evidence and rationale.

The blank worksheet contains prompts only.

Reference · original teaching example

Purpose: Planning question

Expected artifact: Planning question

Set which decisions will be supported without item estimates and define service boundary.

Entry:

...

  • Is it clear whether answering quantity-by-date or date-for-quantity?

Slice: Flowable items

Expected artifact: Flowable items

Shape work into small valuable items with one Done and explicit split policy.

Entry:

...

  • Are large or unlike items detectable and splittable before pull?

Control: Flow system

Expected artifact: Flow system

Operate pull, WIP limits, replenishment, and blocker policy; reprioritise scope continuously.

Entry:

...

  • Are new items pulled only with capacity and blockers actively ended?

Measure: Flow data

Expected artifact: Flow data

Capture throughput, cycle time, WIP, and item age from consistent start/Done events.

Entry:

...

  • Is data complete, service-consistent, and free from retrospective redefinition?

Forecast: Delivery forecast

Expected artifact: Delivery forecast

Forecast remaining count probabilistically from historical flow and test scope/WIP scenarios.

Entry:

...

  • Does forecast state quantile, data window, and assumptions?

Learn: Flow decision

Expected artifact: Flow decision

Compare forecast to actual, decide scope/policies, and justify new data window after system change.

Entry:

...

  • Does variance lead to a scope, WIP, or capability decision?

Open questions and next steps

...

Method guide: https://methodatlas.meierhoff-systems.de/en/methods/no-estimates/run-sheet

06

Example output

Concrete filled scenario, fictional example

Fictional example for orientation. Counts and timings describe this case and are not universal requirements.

no-estimates-beispiel.md

Concrete filled scenario, fictional example

Fictional teaching example: NoEstimates · working structure

System elementPolicy/dataFictional stateDecision signalActionOwnerReview
Item slicingSplit policyValuable; one Done; provider migration in 4 itemsItem age >8 daysSplit before pullProduct + teamReplenishment
WIP/pullPull policyLimit 4; pull only with free slotBlocked >2 daysAssign swarm ownerFlow managerDaily
Flow data12-week windowThroughput 5,7,4,6,8,5,0,7,6,4,9,5Zero week remains a sampleRecord cause; retain valueData ownerWeekly
ForecastDate for quantity30 items: P50 6, P85 7, P95 8 weeksP85 drifts beyond 8 weeksDecide scope/WIPService ownerWeekly
07

Pitfalls

Recognize symptoms and steer against them

Quick overview, the full text is in the matching phase in the flow

  1. Planning questionNoEstimates is declared an end to planning.View in phase
  2. Flowable itemsLarge epics flow as one counted item and distort data.View in phase
  3. Flow systemWithout estimation meetings, work starts without planning.View in phase
  4. Flow dataAverage velocity simply replaces item estimates.View in phase
  5. Delivery forecastOne date is presented as a commitment.View in phase
  6. Flow decisionThe team improves estimate values rather than flow.View in phase
08

Stop criteria

When to pause, resolve prerequisites, or choose another approach

Stop Purpose: Is it clear whether answering quantity-by-date or date-for-quantity?
Stop Slice: Are large or unlike items detectable and splittable before pull?
Stop Control: Are new items pulled only with capacity and blockers actively ended?
Stop Measure: Is data complete, service-consistent, and free from retrospective redefinition?
Stop Forecast: Does forecast state quantile, data window, and assumptions?
Stop Learn: Does variance lead to a scope, WIP, or capability decision?

Method after Vasco Duarte and Woody Zuill. Curated with 1 sources.

Sources: NoEstimates

Flow, timeboxes, and pitfalls are editorially curated practice recommendations based on the listed sources.

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